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#! /usr/bin/python
import copy
import sys
from svmutil import *
import numpy as np
import matplotlib.pyplot as plt
from sets import Set
import itertools
def normalize(a,weights=None):
if weights == None:
weights= {}
cols = a.shape[1]
for i in range(cols):
weights[i] = None
for i in weights.keys():
column = a[:,i]
if weights[i] == None:
weights[i] = np.mean(column), np.std(column)
a[:,i] = (column-weights[i][0])/weights[i][1]
return a,weights
def read_filter(filename) :
a = np.loadtxt(filename,comments="#",delimiter=",",
usecols=(1,4,5,6,7,8,9,10,11,12,13,14,15))
#remove rows with missing values, filter data
a = np.ma.masked_equal(a,-1)
a = np.ma.mask_rows(a)
a = np.ma.compress_rows(a)
distance = a[:,1]
variance = a[:,2]
diff = a[:,3]
a = a[(distance>2) & (distance<3.2) & (diff<0.5)]
return a
def normalize_filter(a,weights=None,nameset=None):
a = np.ma.masked_array(a)
#normalize data
if weights==None:
weights = dict(zip(range(4,13),[None for i in range(9)]))
a,weights = normalize(a,weights)
if nameset != None:
for i in range(len(a)):
if int(a[i][0]) not in nameset:
a.mask[i][0] = True
a = np.ma.mask_rows(a)
a = np.ma.compress_rows(a)
return list(a[:,0]),[dict(zip(range(1,11),r)) for r in a[:,4:]],weights
train_filename = sys.argv[1]
test_filename = sys.argv[2]
log_filename = open(sys.argv[3],"w")
a = read_filter(train_filename)
b = read_filter(test_filename)
main_set = Set(range(1,26)).difference(Set([13,19,3]))
def accuracy_subsets(n):
for s in itertools.combinations(main_set,n):
y1,x1,weights = normalize_filter(a,nameset=s)
model = svm_train(y1,x1)
y2,x2,weights = normalize_filter(b,weights=weights,nameset=s)
p_labels,p_acc,p_vals = svm_predict(y2,x2,model)
log_filename.write(str(n)+"#"+str(s)+"#"+str(p_acc[0])+"\n")
log_filename.flush()
#y1,x1,weights = normalize_filter(a,nameset=main_set)
#model = svm_train(y1,x1)
#y2,x2,weights = normalize_filter(b,weights=weights,nameset=main_set)
#p_labels,p_acc,p_vals = svm_predict(y2,x2,model)
for i in range(2,6):
accuracy_subsets(i)
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